Alternativas para la medición partimos de una idea elemental: exportaciones por trabajador como proxy de la productividad ES posible que ahí las magnitudes jueguen un rol que rompa cosas por lo que mejor ponerlo en términos relativos

$\dfrac{X_{HS;s;p}}{L_{s,p}$

dos pistas que nos pueden ayudar a pensar:

$VCR = \dfrac{\dfrac{X_{HS;p}} {X_{HS;w}}} {\dfrac{X_p}{X_w}}$


Reescriendo también podría pensar en:

$LCR = \dfrac{\dfrac{L_{s;p}}{L_{s;w}}} {\dfrac{L_p}{L_w}}$


capital/trabajo intensivo:

$intensidad = \dfrac{\dfrac{VA_{s;p}}{VA_p}} {\dfrac{L_{s;p}}{L_p}}$


**opción 1**

$\dfrac{\dfrac{X_{HS;s;p}}{X_p}} {\dfrac{L_{s,p}}{L_p}}$


**opción 2**

$\dfrac{\dfrac{X_{HS;s;p}}{X_{HS;s;w}}} {\dfrac{L_{s,p}}{L_{s,w}}}$

opción 3
$\dfrac{VCR}{LCR}$

Preparación de bases¶

t country k v expos_totales_pais expos_mundo_producto expos_totales_mundo prop_expo VCR VCR_norm VCR_existe description
1 2002 af 80232 512.835 45183.569 302843.310 6.404218e+09 0.011350 240.018790 0.991702 1.0 Nuts, edible: walnuts, fresh or dried, shelled
2 2002 af 12111 819.028 45183.569 29169.439 6.404218e+09 0.018127 3979.754121 0.999498 1.0 Liquorice roots used primarily in perfumery, i...
3 2002 af 12119 80.016 45183.569 880996.538 6.404218e+09 0.001771 12.873250 0.855838 1.0 Plants and parts (including seeds and fruits) ...
4 2002 af 130212 1.155 45183.569 84206.964 6.404218e+09 0.000026 1.944104 0.320676 1.0 Vegetable saps and extracts: of liquorice
6 2002 af 540769 15.181 45183.569 848628.830 6.404218e+09 0.000336 2.535527 0.434313 1.0 Fabrics, woven: containing less than 85% by we...

Definiciones¶

v prop_hs_pais prop_hs_mundo prop_pais_mundo
v 1.000000 0.217609 0.430533 -0.007933
prop_hs_pais 0.217609 1.000000 0.075663 0.212244
prop_hs_mundo 0.430533 0.075663 1.000000 -0.016211
prop_pais_mundo -0.007933 0.212244 -0.016211 1.000000
###############################    0       #########################
###############################    1       #########################
###############################    2       #########################
###############################    3       #########################
###############################    4       #########################
###############################    5       #########################
###############################    6       #########################
###############################    7       #########################
###############################    8       #########################
###############################    9       #########################
runs parametros kl_score best
0 0 {'perplexity': 15, 'metric': 'cosine'} 0.290841 TSNE(metric='cosine', n_iter=5000, perplexity=...
1 0 {'perplexity': 70, 'metric': 'correlation'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
2 0 {'perplexity': 70, 'metric': 'cosine'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
3 0 {'perplexity': 55, 'metric': 'cosine'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
4 0 {'perplexity': 60, 'metric': 'correlation'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
... ... ... ... ...
95 9 {'perplexity': 20, 'metric': 'cosine'} 0.254610 TSNE(metric='cosine', n_iter=5000, perplexity=...
96 9 {'perplexity': 20, 'metric': 'correlation'} 0.330467 TSNE(metric='cosine', n_iter=5000, perplexity=...
97 9 {'perplexity': 25, 'metric': 'correlation'} 0.315320 TSNE(metric='cosine', n_iter=5000, perplexity=...
98 9 {'perplexity': 60, 'metric': 'cosine'} 0.595184 TSNE(metric='cosine', n_iter=5000, perplexity=...
99 9 {'perplexity': 35, 'metric': 'cosine'} 0.431405 TSNE(metric='cosine', n_iter=5000, perplexity=...

100 rows × 4 columns

1
2
3
4
5
6
runs parametros kl_score best
0 0 {'perplexity': 15, 'metric': 'cosine'} 0.290841 TSNE(metric='cosine', n_iter=5000, perplexity=...
1 0 {'perplexity': 70, 'metric': 'correlation'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
2 0 {'perplexity': 70, 'metric': 'cosine'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
3 0 {'perplexity': 55, 'metric': 'cosine'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
4 0 {'perplexity': 60, 'metric': 'correlation'} 0.594407 TSNE(metric='cosine', n_iter=5000, perplexity=...
... ... ... ... ...
95 9 {'perplexity': 20, 'metric': 'cosine'} 0.254610 TSNE(metric='cosine', n_iter=5000, perplexity=...
96 9 {'perplexity': 20, 'metric': 'correlation'} 0.330467 TSNE(metric='cosine', n_iter=5000, perplexity=...
97 9 {'perplexity': 25, 'metric': 'correlation'} 0.315320 TSNE(metric='cosine', n_iter=5000, perplexity=...
98 9 {'perplexity': 60, 'metric': 'cosine'} 0.595184 TSNE(metric='cosine', n_iter=5000, perplexity=...
99 9 {'perplexity': 35, 'metric': 'cosine'} 0.431405 TSNE(metric='cosine', n_iter=5000, perplexity=...

100 rows × 4 columns

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[77], line 3
      1 new_sector1 = new_sector.merge(empleo[['country', 'time', 'cod_numeric', 'empleo' ]], left_on=['country', 't' , 'CIIU_2d'], right_on=['country', 'time', 'cod_numeric'], how= 'left')
      2 new_sector1['productividad'] = new_sector1.v/new_sector1.empleo
----> 3 plot_clusters_productivity(new_sector1, 'productividad')

TypeError: plot_clusters_productivity() missing 1 required positional argument: 'df_cluster'